Abstract

Journal of Near Infrared Spectroscopy
Volume 17 Issue 5, Pages 265–273 (2009)
doi: 10.1255/jnirs.848

Use of near infrared spectroscopy to discriminate between and predict the nutrient composition of different species and parts of bamboo: application for studying giant panda foraging ecology

E. Wiedower,^a R. Hansen,^b H. Bissell,^b R. Ouellette,^b A. Kouba,^b J. Stuth,^a B. Rude^c and D. Tolleson^a,d,*
aTexas A&M University, Grazingland Animal Nutrition Laboratory, 2126 TAMU, College Station, TX 77845-2126, USA
^bMemphis Zoo, Conservation and Research Department, 2000 Prentiss Place, Memphis, TN 38112, USA
^cMississippi State University, Animal and Dairy Sciences, Box 9815, Mississippi State, MS 39762, USA
^dArizona Agricultural Experiment Station, V Bar V Ranch, 2657 South Village Drive, Cottonwood, AZ 86326, USA. E-mail: dougt@cals.arizona.edu

Giant pandas (Ailuropoda melanoleuca) are specialist feeders, dependent upon bamboo as their main dietary resource. Due to the difficulty of many captive facilities to meet the natural qualitative diet changes in bamboo species and plant parts consumed seasonally by giant pandas, it is important to understand the nutritional quality of this forage and the differences among plant parts for improved husbandry. Near infrared (NIR) reflectance spectroscopy has been used as a tool to measure forage quality for both domestic and free-ranging species. The objective of this study was to determine the capability of NIR spectroscopy to: (1) discriminate among bamboo parts; (2) discriminate among bamboo species; and (3) to predict the nutrient composition of bamboo. All bamboo samples were received from the Memphis Zoo Bamboo Farm (Memphis, TN, USA), dried at 60°C and ground to pass through a 1 mm screen before analysis. Discrimination between a total of 722 branch, culm, and leaf samples resulted in an R² of 0.88 and SECV of 0.18. Spectra from a total of 756 samples of 4 different species were used to create a discriminant equation among bamboo species. This resulted in an R² of 0.47 and SECV of 0.29. Validation sets were correctly predicted at the following rates: part branch 94%, culm 100%, and leaf 100%; species Phyllostachys aurea 10%, P. aureosulcata 98%, P. glauca 80%, and Pseudosasa japonica 73%. Calibration equations for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), and organic matter (OM) were created using all bamboo samples. For each nutritional constituent, the calibration R² values exceeded 0.96. The average SEP across all constituents was 0.21% for CP, 2.35% for NDF, 3.62% for ADF, 0.84% for DM, and 0.25% for OM. NIR spectroscopy was used to predict nutrient characteristics and discriminate between bamboo plant parts and species. The inability to discriminate among bamboo species is most likely due to a close physiological similarity between at least 2 of the species. Results suggest that NIR spectroscopy can be used to analyse bamboo forage quality which may have applications to captive giant panda husbandry.

Keywords: NIR spectroscopy, bamboo, giant panda, nutrition, Phyllostachys, Pseudososa

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